Upload models.py

models.py

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+import torch
+from transformers import BertTokenizer
+from torch import nn
+from transformers import BertModel
+
+
+#CustomMBERTModel= torch.load("/data2/Akash_for_interface/model_mbert_1416.pt")
+tokenizer = BertTokenizer.from_pretrained('bert-base-multilingual-cased')
+sentences= "you are good  person."
+max_len=len(sentences)
+encoding = tokenizer.encode_plus(sentences,add_special_tokens=True,max_length=max_len,padding='max_length',truncation=True,return_tensors='pt')
+input_ids=encoding['input_ids'].flatten(),
+attention_mask= encoding['attention_mask'].flatten()
+#print(input_ids[0])
+
+labels=["Non Hateful","Hateful"]
+
+
+
+device = 'cpu'
+class CustomMBERTModel(nn.Module):
+    def __init__(self, num_labels):
+        super(CustomMBERTModel, self).__init__()
+        self.bert = BertModel.from_pretrained('bert-base-multilingual-cased')
+        
+        # Freeze all layers except the top 2
+        for param in self.bert.parameters():
+            param.requires_grad = False
+
+        # Unfreeze the parameters of the top 2 layers
+        for param in self.bert.encoder.layer[-2:].parameters():
+            param.requires_grad = True
+
+        # Adding Linear layer
+        self.linear = nn.Linear(self.bert.config.hidden_size, num_labels)
+
+    def forward(self, input_ids, attention_mask):
+        outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask)
+        hidden_states = outputs.last_hidden_state
+
+        pooled_output= hidden_states[:,0,:]
+        pooled_output=torch.squeeze(pooled_output,dim=1)
+
+        #print('p-shape:', pooled_output.shape)
+
+        logits = self.linear(pooled_output)
+
+        #print('l-shape:', logits.shape)
+        return logits
+